The timely measure of information associated with an agricultural crop, including its emergent plant and weed densities, its current health, its projected health trajectory, the presence of invasive species or other damaging conditions, and/or other parameters are of prime importance to agricultural producers. The aim of the timely measurement of agricultural crop parameters is to maximize yields with the minimum of input costs, waste, and burden to society and environment. This measurement has typically been performed using human labor to survey agricultural fields on a regular schedule as monitors for emerging health conditions. The economics of manual crop scouting, however, limits surveys to select locations within production fields such that large fractions of acreage are never sampled, and problems frequently emerge undetected with losses to crop yield.
Airborne and spaceborne data collection systems have been employed to replace and/or complement human labor in scouting for issues within agricultural crops. Such systems include manned and unmanned aerial systems (UAS). Current systems employ expensive, complex and processor-intensive methods to extract meaningful information from the measurements taken by instruments aboard them.
Airborne imaging systems that are limited to low altitudes and narrow fields of view must collect numerous individual images in order to provide total coverage of a subject target. These individual images (which may number in the thousands depending on field size, altitude, and sensor field of view) are traditionally aggregated into a larger single image mosaic in order to extract meaningful information. This process is commonly referred to as “mosaicking” or “stitching” and existing methods are inadequate for multiple reasons.
Mosaicking algorithms rely on correlating spatial features between and among adjacent images to determine proper relative placement. This is a computationally intensive process that grows exponentially with the number of images in the mosaic.
Mosaicking algorithms were designed to stitch together images containing unique and easily identifiable features and are poorly suited to imagery with repeating spatial patterns as is inherent in agricultural crops. Furthermore, repeating spatial patterns vary by crop type and some crops yield virtually no unique patterns at sampling scales greater than a couple of centimeters. These methods are therefore not very portable from crop to crop and often yield no solution at all.
The effectiveness of a mosaicking algorithm increases with increasing correlation between adjacent images. Mosaicking tools therefore recommend a percentage of scene overlap in the image collection to assure a solution. For agricultural crops, the recommended overlap can be up to 90%, which imposes large costs in terms of the required flight time to complete a survey, the volume of data to process, and the time and processing resources required to compute a solution.
Once relative placement is determined, overlapping regions must be blended to avoid discontinuity artifacts. The selection of an appropriate blending mix is computationally intensive and subjective based on local conditions.
As a specific example, the creation of a mosaic for a typical 160-acre field of corn using traditional mosaicking methods can take 6-8 hours on high-cost, specialized computing resources. Not only are traditional methods inefficient and poorly suited for this task, they are unnecessary. Even if successful in rendering a result, 160 acre full-field mosaics accurate to centimeter scales are no more effective for agricultural management than mosaics with spatial resolutions of meters, provided the crop information is extracted at centimeter scales and can be rendered as a “zoomed-in” view.